Cross-view geo-localization via Salient Feature Partition Network
Sijin He, Yuehuan Wang · Journal of Physics Conference Series · 2021
Abstract Cross-view geo-localization aims to find images containing the same geographic target from images obtained from different platforms. The extreme viewpoint variations bring challenges to this task. Existing methods usually focus on mining the fine-grained features of geographic targets in images, ignoring the potential contextual information around them. In this paper, we consider that the background regions can be used as auxiliary information, which can make the image representation for geo-localization more discriminative. Specifically, we designed a classification network that divides regional features based on saliency, called Salient Feature Partition Network (SFPN), which utilizes background information in an end-to-end manner. Without using additional part estimators, SFPN divides the features into foreground and background based on saliency. It simplifies part matching and realizes the region division learning. The method proposed in this paper has achieved competitive results on the university 1652 dataset.